1. Scientific Research Group in Egypt (SRGE)
Swarm Intelligence (III)
Group search optimizer (GSO)
Scientific Research Group in Egypt (SRGE)
Dr. Ahmed Fouad Ali
Suez Canal University,
Dept. of Computer Science, Faculty of Computers and informatics
Member of the Scientific Research Group in Egypt

2. Scientific Research Group in Egypt

3. Meta-heuristics techniques

4. Outline 1. Group search optimizer (GSO)(Main idea)
2. History of GSO algorithm
3. Group search optimizer (GSO)
4. GSO Algorithm
5. References

5. Group search optimizer GSO (Main idea)
A group can be defined as a structured collection of interacting organisms (or members).
The original idea of GSO comes
from the social behavior of animals foraging and group living theory.
GSO is based on Producer- Scrounger (PS) behavior of group living animals , which assume group members producing (searching for foods) and scrounging (joining resources uncovered by others).

6. History of GSO algorithm
GSO algorithm is a novel swarm intelligence optimization algorithm, first published by He et al (2006).
GSO algorithm is the novel population based nature inspired algorithm, especially animal searching behavior.

7. Group search optimizer (GSO)
The population of the GSO algorithm is called a group and each individual in the population is called a member.
In an n-dimensional search space, the ith member at the kth searching iteration, has
1- a current position Xki ∈ Rn .
2- a head angle ϕki = (ϕk i1, , ϕk i(n−1)) ∈ Rn−1 .
3- a head direction Dk i (ϕki ) = (dk i1, , dk in) ∈ Rn .
which can be calculated from ϕki
via a Polar to Cartesian coordinates
Transformation:

8. Group search optimizer GSO
Dk i (ϕki ) = (dk i1, , dk in) ∈ Rn .

9. Group search optimizer (GSO)
In GSO, a group consists three kinds of members: producers and scroungers whose behaviors are based on the PS model, and rangers who perform random walk motions.
The PS model is simplified by
assuming that there is only one producer at each searching
Iteration and the remaining members are scroungers and rangers.

10. GSO algorithm
In the GSO algorithm, at the kth iteration the producer Xp behaves as follows:
1) The producer will scan at zero degree and then scan laterally by randomly sampling three points in the scanning Field as follows:
Scanning field at 3D space

11. GSO algorithm One point at zero degree:
One point in the right hand side hypercube:
(2)
(3)
Diversification
One point in the left hand side hypercube:
(4)
where r1 ∈ R1 is a normally distributed random number with mean 0 and standard deviation 1 and r2 ∈ Rn−1 is a random sequence in the range (0, 1).

12. GSO algorithm The producer will then find the best point with the best
resource (fitness value). If the best point has a better resource than its current position, then it will fly to this point. Or it will stay in its current position and turn its head to a new angle:
(5)
Where α max is the maximum turning angle.

13. GSO algorithm
If the producer cannot find a better area after a iterations, it will turn its head back to zero degree:
(6)
Where a is a constant.
Intensification
During each searching iteration , a number of group members are selected as scroungers. The scroungers will keep searching for opportunities to join the resources by random walk toward the producer.
(7)
Where r3 ∈ Rn is a uniform random sequence in the range
(0, 1).

14. GSO algorithm Eventually, random walks, are employed by rangers.
If the ith group member is selected as a ranger, at the kth iteration it generates a random head angle ϕi:
(8)
where αmax is the maximum turning angle; and (2) it chooses a random distance:
And move to the new point
(9)

15. GSO algorithm

16. References
Computational Intelligence An Introduction
Andries P. Engelbrecht, University of Pretoria South Africa
S. He, Q. H. Wu, “A Novel Group Search Optimizer Inspired by Animal Behavioural Ecology”, 2006 IEEE Congress on Evolutionary Computation Sheraton Vancouver Wall Centre Hotel, Vancouver, BC, Canada July 16-21, 2006

17. Thank you